SOTAVerified

Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

Showing 18511900 of 2167 papers

TitleStatusHype
DrishtiKon: Multi-Granular Visual Grounding for Text-Rich Document ImagesCode0
TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning BaselinesCode0
Lightweight Recurrent Cross-modal Encoder for Video Question AnsweringCode0
Learning Visual Question Answering by Bootstrapping Hard AttentionCode0
TAB-VCR: Tags and Attributes based VCR BaselinesCode0
Outside Knowledge Conversational Video (OKCV) Dataset -- Dialoguing over VideosCode0
Learning to Reason: End-to-End Module Networks for Visual Question AnsweringCode0
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question AnsweringCode0
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question AnsweringCode0
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic ReconstructionCode0
Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar InstancesCode0
Learning to Model and Ignore Dataset Bias with Mixed Capacity EnsemblesCode0
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMsCode0
VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-TuningCode0
Learning to Count Objects in Natural Images for Visual Question AnsweringCode0
Are VLMs Really BlindCode0
Learning to Collocate Visual-Linguistic Neural Modules for Image CaptioningCode0
Learning Representations of Sets through Optimized PermutationsCode0
TallyQA: Answering Complex Counting QuestionsCode0
Learning from Lexical Perturbations for Consistent Visual Question AnsweringCode0
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ DocumentsCode0
Learning Convolutional Text Representations for Visual Question AnsweringCode0
Learning content and context with language bias for Visual Question AnsweringCode0
Learning Conditioned Graph Structures for Interpretable Visual Question AnsweringCode0
DLaVA: Document Language and Vision Assistant for Answer Localization with Enhanced Interpretability and TrustworthinessCode0
P NP, at least in Visual Question AnsweringCode0
Visual Reasoning with Multi-hop Feature ModulationCode0
Learning by Abstraction: The Neural State MachineCode0
VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question AnsweringCode0
LayoutLMv3: Pre-training for Document AI with Unified Text and Image MaskingCode0
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document UnderstandingCode0
Patent Figure Classification using Large Vision-language ModelsCode0
Task Formulation Matters When Learning Continually: A Case Study in Visual Question AnsweringCode0
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural NetworksCode0
Visuo-Linguistic Question Answering (VLQA) ChallengeCode0
Latent Alignment and Variational AttentionCode0
ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in VietnameseCode0
ViConsFormer: Constituting Meaningful Phrases of Scene Texts using Transformer-based Method in Vietnamese Text-based Visual Question AnsweringCode0
Large Models in Dialogue for Active Perception and Anomaly DetectionCode0
Large Language Models Understand LayoutCode0
Cascaded Mutual Modulation for Visual ReasoningCode0
Language-Conditioned Graph Networks for Relational ReasoningCode0
CARETS: A Consistency And Robustness Evaluative Test Suite for VQACode0
Perceptual Score: What Data Modalities Does Your Model Perceive?Code0
TCC-Bench: Benchmarking the Traditional Chinese Culture Understanding Capabilities of MLLMsCode0
Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal EndoscopyCode0
Kvasir-VQA: A Text-Image Pair GI Tract DatasetCode0
Bridging Languages through Images with Deep Partial Canonical Correlation AnalysisCode0
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality AssessmentCode0
KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean LanguageCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04Unverified
3TRRNet (Ensemble)Accuracy74.03Unverified
4MIL-nbgaoAccuracy73.81Unverified
5Kakao BrainAccuracy73.33Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14Unverified
7270Accuracy70.23Unverified
8NSM ensemble (updated)Accuracy67.55Unverified
9VinVL-DPTAccuracy64.92Unverified
10VinVL+LAccuracy64.85Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3Unverified
2BEiT-3Accuracy84.19Unverified
3VLMoAccuracy82.78Unverified
4ONE-PEACEAccuracy82.6Unverified
5mPLUG (Huge)Accuracy82.43Unverified
6CuMo-7BAccuracy82.2Unverified
7X2-VLM (large)Accuracy81.9Unverified
8MMUAccuracy81.26Unverified
9LyricsAccuracy81.2Unverified
10InternVL-CAccuracy81.2Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03Unverified
2mPLUG-Hugeoverall83.62Unverified
3ONE-PEACEoverall82.52Unverified
4X2-VLM (large)overall81.8Unverified
5VLMooverall81.3Unverified
6SimVLMoverall80.34Unverified
7X2-VLM (base)overall80.2Unverified
8VASToverall80.19Unverified
9VALORoverall78.62Unverified
10Prompt Tuningoverall78.53Unverified